<<<<<<< Updated upstream Pandas Profiling Report

Overview

Dataset statistics

Number of variables28
Number of observations22568
Missing cells22568
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 MiB
Average record size in memory224.0 B

Variable types

Categorical11
DateTime1
TimeSeries15
Numeric1

Alerts

State Code has constant value "4" Constant
County Code has constant value "13" Constant
Site Num has constant value "3002" Constant
Address has constant value "1645 E ROOSEVELT ST-CENTRAL PHOENIX STN" Constant
State has constant value "Arizona" Constant
County has constant value "Maricopa" Constant
City has constant value "Phoenix" Constant
NO2 Units has constant value "Parts per billion" Constant
O3 Units has constant value "Parts per million" Constant
SO2 Units has constant value "Parts per billion" Constant
CO Units has constant value "Parts per million" Constant
CO 1st Max Hour is highly correlated with NO2 1st Max Hour and 1 other fieldsHigh correlation
State Code is highly correlated with Site Num and 9 other fieldsHigh correlation
County Code is highly correlated with State Code and 9 other fieldsHigh correlation
Site Num is highly correlated with State Code and 9 other fieldsHigh correlation
Address is highly correlated with State Code and 9 other fieldsHigh correlation
State is highly correlated with State Code and 9 other fieldsHigh correlation
County is highly correlated with State Code and 9 other fieldsHigh correlation
City is highly correlated with State Code and 9 other fieldsHigh correlation
NO2 Units is highly correlated with State Code and 9 other fieldsHigh correlation
O3 Units is highly correlated with State Code and 9 other fieldsHigh correlation
SO2 Units is highly correlated with State Code and 9 other fieldsHigh correlation
CO Units is highly correlated with State Code and 9 other fieldsHigh correlation
NO2 Mean is highly correlated with NO2 1st Max Value and 7 other fieldsHigh correlation
NO2 1st Max Value is highly correlated with NO2 Mean and 4 other fieldsHigh correlation
NO2 AQI is highly correlated with NO2 1st Max Hour and 6 other fieldsHigh correlation
O3 Mean is highly correlated with NO2 Mean and 3 other fieldsHigh correlation
O3 1st Max Value is highly correlated with NO2 1st Max Hour and 7 other fieldsHigh correlation
O3 AQI is highly correlated with NO2 1st Max Hour and 7 other fieldsHigh correlation
SO2 Mean is highly correlated with SO2 1st Max Value and 1 other fieldsHigh correlation
SO2 1st Max Value is highly correlated with NO2 AQI and 6 other fieldsHigh correlation
SO2 AQI is highly correlated with NO2 AQI and 5 other fieldsHigh correlation
CO Mean is highly correlated with NO2 Mean and 6 other fieldsHigh correlation
CO 1st Max Value is highly correlated with NO2 1st Max Hour and 6 other fieldsHigh correlation
CO AQI is highly correlated with NO2 1st Max Hour and 6 other fieldsHigh correlation
NO2 1st Max Hour is highly correlated with NO2 AQI and 7 other fieldsHigh correlation
O3 1st Max Hour is highly correlated with NO2 1st Max Hour and 2 other fieldsHigh correlation
SO2 1st Max Hour is highly correlated with NO2 1st Max Hour and 2 other fieldsHigh correlation
SO2 AQI has 11281 (50.0%) missing values Missing
CO AQI has 11287 (50.0%) missing values Missing
NO2 Mean is non stationary Non stationary
NO2 1st Max Value is non stationary Non stationary
NO2 1st Max Hour is non stationary Non stationary
NO2 AQI is non stationary Non stationary
O3 Mean is non stationary Non stationary
O3 1st Max Value is non stationary Non stationary
O3 1st Max Hour is non stationary Non stationary
O3 AQI is non stationary Non stationary
SO2 Mean is non stationary Non stationary
SO2 1st Max Value is non stationary Non stationary
SO2 1st Max Hour is non stationary Non stationary
SO2 AQI is non stationary Non stationary
CO Mean is non stationary Non stationary
CO 1st Max Value is non stationary Non stationary
CO AQI is non stationary Non stationary
NO2 Mean is seasonal Seasonal
NO2 1st Max Value is seasonal Seasonal
NO2 1st Max Hour is seasonal Seasonal
NO2 AQI is seasonal Seasonal
O3 Mean is seasonal Seasonal
O3 1st Max Value is seasonal Seasonal
O3 1st Max Hour is seasonal Seasonal
O3 AQI is seasonal Seasonal
SO2 Mean is seasonal Seasonal
SO2 1st Max Value is seasonal Seasonal
SO2 1st Max Hour is seasonal Seasonal
SO2 AQI is seasonal Seasonal
CO Mean is seasonal Seasonal
CO 1st Max Value is seasonal Seasonal
CO AQI is seasonal Seasonal
NO2 1st Max Hour has 3220 (14.3%) zeros Zeros
SO2 Mean has 708 (3.1%) zeros Zeros
SO2 1st Max Value has 708 (3.1%) zeros Zeros
SO2 1st Max Hour has 2732 (12.1%) zeros Zeros
SO2 AQI has 384 (1.7%) zeros Zeros
CO 1st Max Hour has 4213 (18.7%) zeros Zeros

Reproduction

Analysis started2022-10-20 17:51:41.841630
Analysis finished2022-10-20 17:52:20.560013
Duration38.72 seconds
Software versionpandas-profiling v3.3.1
Download configurationconfig.json

Variables

State Code
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
4
22568 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22568
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
422568
100.0%

Length

2022-10-20T18:52:20.644557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Overview

Dataset statistics

Number of variables28
Number of observations22568
Missing cells22568
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 MiB
Average record size in memory224.0 B

Variable types

Categorical11
DateTime1
TimeSeries15
Numeric1

Alerts

State Code has constant value "4" Constant
County Code has constant value "13" Constant
Site Num has constant value "3002" Constant
Address has constant value "1645 E ROOSEVELT ST-CENTRAL PHOENIX STN" Constant
State has constant value "Arizona" Constant
County has constant value "Maricopa" Constant
City has constant value "Phoenix" Constant
NO2 Units has constant value "Parts per billion" Constant
O3 Units has constant value "Parts per million" Constant
SO2 Units has constant value "Parts per billion" Constant
CO Units has constant value "Parts per million" Constant
CO 1st Max Hour is highly correlated with NO2 1st Max Hour and 1 other fieldsHigh correlation
State Code is highly correlated with Address and 9 other fieldsHigh correlation
County Code is highly correlated with Address and 9 other fieldsHigh correlation
Site Num is highly correlated with Address and 9 other fieldsHigh correlation
Address is highly correlated with State and 9 other fieldsHigh correlation
State is highly correlated with Address and 9 other fieldsHigh correlation
County is highly correlated with Address and 9 other fieldsHigh correlation
City is highly correlated with Address and 9 other fieldsHigh correlation
NO2 Units is highly correlated with Address and 9 other fieldsHigh correlation
O3 Units is highly correlated with Address and 9 other fieldsHigh correlation
SO2 Units is highly correlated with Address and 9 other fieldsHigh correlation
CO Units is highly correlated with Address and 9 other fieldsHigh correlation
NO2 Mean is highly correlated with NO2 1st Max Value and 7 other fieldsHigh correlation
NO2 1st Max Value is highly correlated with NO2 Mean and 4 other fieldsHigh correlation
NO2 AQI is highly correlated with NO2 1st Max Hour and 6 other fieldsHigh correlation
O3 Mean is highly correlated with NO2 Mean and 3 other fieldsHigh correlation
O3 1st Max Value is highly correlated with NO2 1st Max Hour and 7 other fieldsHigh correlation
O3 AQI is highly correlated with NO2 1st Max Hour and 7 other fieldsHigh correlation
SO2 Mean is highly correlated with SO2 1st Max Value and 1 other fieldsHigh correlation
SO2 1st Max Value is highly correlated with NO2 AQI and 6 other fieldsHigh correlation
SO2 AQI is highly correlated with NO2 AQI and 5 other fieldsHigh correlation
CO Mean is highly correlated with NO2 Mean and 6 other fieldsHigh correlation
CO 1st Max Value is highly correlated with NO2 1st Max Hour and 6 other fieldsHigh correlation
CO AQI is highly correlated with NO2 1st Max Hour and 6 other fieldsHigh correlation
NO2 1st Max Hour is highly correlated with NO2 AQI and 7 other fieldsHigh correlation
O3 1st Max Hour is highly correlated with NO2 1st Max Hour and 2 other fieldsHigh correlation
SO2 1st Max Hour is highly correlated with NO2 1st Max Hour and 2 other fieldsHigh correlation
SO2 AQI has 11281 (50.0%) missing values Missing
CO AQI has 11287 (50.0%) missing values Missing
NO2 Mean is non stationary Non stationary
NO2 1st Max Value is non stationary Non stationary
NO2 1st Max Hour is non stationary Non stationary
NO2 AQI is non stationary Non stationary
O3 Mean is non stationary Non stationary
O3 1st Max Value is non stationary Non stationary
O3 1st Max Hour is non stationary Non stationary
O3 AQI is non stationary Non stationary
SO2 Mean is non stationary Non stationary
SO2 1st Max Value is non stationary Non stationary
SO2 1st Max Hour is non stationary Non stationary
SO2 AQI is non stationary Non stationary
CO Mean is non stationary Non stationary
CO 1st Max Value is non stationary Non stationary
CO AQI is non stationary Non stationary
NO2 Mean is seasonal Seasonal
NO2 1st Max Value is seasonal Seasonal
NO2 1st Max Hour is seasonal Seasonal
NO2 AQI is seasonal Seasonal
O3 Mean is seasonal Seasonal
O3 1st Max Value is seasonal Seasonal
O3 1st Max Hour is seasonal Seasonal
O3 AQI is seasonal Seasonal
SO2 Mean is seasonal Seasonal
SO2 1st Max Value is seasonal Seasonal
SO2 1st Max Hour is seasonal Seasonal
SO2 AQI is seasonal Seasonal
CO Mean is seasonal Seasonal
CO 1st Max Value is seasonal Seasonal
CO AQI is seasonal Seasonal
NO2 1st Max Hour has 3220 (14.3%) zeros Zeros
SO2 Mean has 708 (3.1%) zeros Zeros
SO2 1st Max Value has 708 (3.1%) zeros Zeros
SO2 1st Max Hour has 2732 (12.1%) zeros Zeros
SO2 AQI has 384 (1.7%) zeros Zeros
CO 1st Max Hour has 4213 (18.7%) zeros Zeros

Reproduction

Analysis started2022-10-20 18:30:16.711891
Analysis finished2022-10-20 18:30:42.320881
Duration25.61 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

State Code
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
4
22568 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22568
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
422568
100.0%

Length

2022-10-20T19:30:42.375457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:52:20.791257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:30:42.449568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
422568
100.0%

Most occurring characters

ValueCountFrequency (%)
422568
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number22568
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
422568
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common22568
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
422568
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII22568
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
422568
100.0%

County Code
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
13
22568 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters45136
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13
2nd row13
3rd row13
4th row13
5th row13

Common Values

ValueCountFrequency (%)
1322568
100.0%

Length

2022-10-20T18:52:20.925050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
422568
100.0%

Most occurring characters

ValueCountFrequency (%)
422568
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number22568
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
422568
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common22568
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
422568
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII22568
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
422568
100.0%

County Code
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
13
22568 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters45136
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13
2nd row13
3rd row13
4th row13
5th row13

Common Values

ValueCountFrequency (%)
1322568
100.0%

Length

2022-10-20T19:30:42.513480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:52:21.045046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:30:42.581217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1322568
100.0%

Most occurring characters

ValueCountFrequency (%)
122568
50.0%
322568
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number45136
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
122568
50.0%
322568
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common45136
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
122568
50.0%
322568
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII45136
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
122568
50.0%
322568
50.0%

Site Num
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
3002
22568 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters90272
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3002
2nd row3002
3rd row3002
4th row3002
5th row3002

Common Values

ValueCountFrequency (%)
300222568
100.0%

Length

2022-10-20T18:52:21.156488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1322568
100.0%

Most occurring characters

ValueCountFrequency (%)
122568
50.0%
322568
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number45136
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
122568
50.0%
322568
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common45136
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
122568
50.0%
322568
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII45136
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
122568
50.0%
322568
50.0%

Site Num
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
3002
22568 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters90272
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3002
2nd row3002
3rd row3002
4th row3002
5th row3002

Common Values

ValueCountFrequency (%)
300222568
100.0%

Length

2022-10-20T19:30:42.641862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:52:21.288551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:30:42.706327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
300222568
100.0%

Most occurring characters

ValueCountFrequency (%)
045136
50.0%
322568
25.0%
222568
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number90272
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
045136
50.0%
322568
25.0%
222568
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common90272
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
045136
50.0%
322568
25.0%
222568
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII90272
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
045136
50.0%
322568
25.0%
222568
25.0%

Address
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
22568 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters880152
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
2nd row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
3rd row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
4th row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
5th row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN

Common Values

ValueCountFrequency (%)
1645 E ROOSEVELT ST-CENTRAL PHOENIX STN22568
100.0%

Length

2022-10-20T18:52:21.411603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
300222568
100.0%

Most occurring characters

ValueCountFrequency (%)
045136
50.0%
322568
25.0%
222568
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number90272
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
045136
50.0%
322568
25.0%
222568
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common90272
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
045136
50.0%
322568
25.0%
222568
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII90272
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
045136
50.0%
322568
25.0%
222568
25.0%

Address
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
22568 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters880152
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
2nd row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
3rd row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
4th row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
5th row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN

Common Values

ValueCountFrequency (%)
1645 E ROOSEVELT ST-CENTRAL PHOENIX STN22568
100.0%

Length

2022-10-20T19:30:42.763900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:52:21.534668image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:30:42.831500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
164522568
16.7%
e22568
16.7%
roosevelt22568
16.7%
st-central22568
16.7%
phoenix22568
16.7%
stn22568
16.7%

Most occurring characters

ValueCountFrequency (%)
112840
12.8%
E112840
12.8%
T90272
 
10.3%
O67704
 
7.7%
S67704
 
7.7%
N67704
 
7.7%
L45136
 
5.1%
R45136
 
5.1%
C22568
 
2.6%
I22568
 
2.6%
Other values (10)225680
25.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter654472
74.4%
Space Separator112840
 
12.8%
Decimal Number90272
 
10.3%
Dash Punctuation22568
 
2.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E112840
17.2%
T90272
13.8%
O67704
10.3%
S67704
10.3%
N67704
10.3%
L45136
 
6.9%
R45136
 
6.9%
C22568
 
3.4%
I22568
 
3.4%
H22568
 
3.4%
Other values (4)90272
13.8%
Decimal Number
ValueCountFrequency (%)
122568
25.0%
622568
25.0%
522568
25.0%
422568
25.0%
Space Separator
ValueCountFrequency (%)
112840
100.0%
Dash Punctuation
ValueCountFrequency (%)
-22568
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin654472
74.4%
Common225680
 
25.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
E112840
17.2%
T90272
13.8%
O67704
10.3%
S67704
10.3%
N67704
10.3%
L45136
 
6.9%
R45136
 
6.9%
C22568
 
3.4%
I22568
 
3.4%
H22568
 
3.4%
Other values (4)90272
13.8%
Common
ValueCountFrequency (%)
112840
50.0%
122568
 
10.0%
-22568
 
10.0%
622568
 
10.0%
522568
 
10.0%
422568
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII880152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
112840
12.8%
E112840
12.8%
T90272
 
10.3%
O67704
 
7.7%
S67704
 
7.7%
N67704
 
7.7%
L45136
 
5.1%
R45136
 
5.1%
C22568
 
2.6%
I22568
 
2.6%
Other values (10)225680
25.6%

State
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
Arizona
22568 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters157976
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArizona
2nd rowArizona
3rd rowArizona
4th rowArizona
5th rowArizona

Common Values

ValueCountFrequency (%)
Arizona22568
100.0%

Length

2022-10-20T18:52:21.649377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
164522568
16.7%
e22568
16.7%
roosevelt22568
16.7%
st-central22568
16.7%
phoenix22568
16.7%
stn22568
16.7%

Most occurring characters

ValueCountFrequency (%)
112840
12.8%
E112840
12.8%
T90272
 
10.3%
O67704
 
7.7%
S67704
 
7.7%
N67704
 
7.7%
L45136
 
5.1%
R45136
 
5.1%
C22568
 
2.6%
I22568
 
2.6%
Other values (10)225680
25.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter654472
74.4%
Space Separator112840
 
12.8%
Decimal Number90272
 
10.3%
Dash Punctuation22568
 
2.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E112840
17.2%
T90272
13.8%
O67704
10.3%
S67704
10.3%
N67704
10.3%
L45136
 
6.9%
R45136
 
6.9%
C22568
 
3.4%
I22568
 
3.4%
H22568
 
3.4%
Other values (4)90272
13.8%
Decimal Number
ValueCountFrequency (%)
122568
25.0%
622568
25.0%
522568
25.0%
422568
25.0%
Space Separator
ValueCountFrequency (%)
112840
100.0%
Dash Punctuation
ValueCountFrequency (%)
-22568
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin654472
74.4%
Common225680
 
25.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
E112840
17.2%
T90272
13.8%
O67704
10.3%
S67704
10.3%
N67704
10.3%
L45136
 
6.9%
R45136
 
6.9%
C22568
 
3.4%
I22568
 
3.4%
H22568
 
3.4%
Other values (4)90272
13.8%
Common
ValueCountFrequency (%)
112840
50.0%
122568
 
10.0%
-22568
 
10.0%
622568
 
10.0%
522568
 
10.0%
422568
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII880152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
112840
12.8%
E112840
12.8%
T90272
 
10.3%
O67704
 
7.7%
S67704
 
7.7%
N67704
 
7.7%
L45136
 
5.1%
R45136
 
5.1%
C22568
 
2.6%
I22568
 
2.6%
Other values (10)225680
25.6%

State
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
Arizona
22568 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters157976
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArizona
2nd rowArizona
3rd rowArizona
4th rowArizona
5th rowArizona

Common Values

ValueCountFrequency (%)
Arizona22568
100.0%

Length

2022-10-20T19:30:42.893560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:52:21.769090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:30:42.957828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
arizona22568
100.0%

Most occurring characters

ValueCountFrequency (%)
A22568
14.3%
r22568
14.3%
i22568
14.3%
z22568
14.3%
o22568
14.3%
n22568
14.3%
a22568
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter135408
85.7%
Uppercase Letter22568
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r22568
16.7%
i22568
16.7%
z22568
16.7%
o22568
16.7%
n22568
16.7%
a22568
16.7%
Uppercase Letter
ValueCountFrequency (%)
A22568
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin157976
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A22568
14.3%
r22568
14.3%
i22568
14.3%
z22568
14.3%
o22568
14.3%
n22568
14.3%
a22568
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII157976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A22568
14.3%
r22568
14.3%
i22568
14.3%
z22568
14.3%
o22568
14.3%
n22568
14.3%
a22568
14.3%

County
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
Maricopa
22568 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters180544
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaricopa
2nd rowMaricopa
3rd rowMaricopa
4th rowMaricopa
5th rowMaricopa

Common Values

ValueCountFrequency (%)
Maricopa22568
100.0%

Length

2022-10-20T18:52:21.896694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
arizona22568
100.0%

Most occurring characters

ValueCountFrequency (%)
A22568
14.3%
r22568
14.3%
i22568
14.3%
z22568
14.3%
o22568
14.3%
n22568
14.3%
a22568
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter135408
85.7%
Uppercase Letter22568
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r22568
16.7%
i22568
16.7%
z22568
16.7%
o22568
16.7%
n22568
16.7%
a22568
16.7%
Uppercase Letter
ValueCountFrequency (%)
A22568
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin157976
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A22568
14.3%
r22568
14.3%
i22568
14.3%
z22568
14.3%
o22568
14.3%
n22568
14.3%
a22568
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII157976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A22568
14.3%
r22568
14.3%
i22568
14.3%
z22568
14.3%
o22568
14.3%
n22568
14.3%
a22568
14.3%

County
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
Maricopa
22568 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters180544
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaricopa
2nd rowMaricopa
3rd rowMaricopa
4th rowMaricopa
5th rowMaricopa

Common Values

ValueCountFrequency (%)
Maricopa22568
100.0%

Length

2022-10-20T19:30:43.014519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:52:22.019798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:30:43.078466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
maricopa22568
100.0%

Most occurring characters

ValueCountFrequency (%)
a45136
25.0%
M22568
12.5%
r22568
12.5%
i22568
12.5%
c22568
12.5%
o22568
12.5%
p22568
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter157976
87.5%
Uppercase Letter22568
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a45136
28.6%
r22568
14.3%
i22568
14.3%
c22568
14.3%
o22568
14.3%
p22568
14.3%
Uppercase Letter
ValueCountFrequency (%)
M22568
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin180544
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a45136
25.0%
M22568
12.5%
r22568
12.5%
i22568
12.5%
c22568
12.5%
o22568
12.5%
p22568
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII180544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a45136
25.0%
M22568
12.5%
r22568
12.5%
i22568
12.5%
c22568
12.5%
o22568
12.5%
p22568
12.5%

City
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
Phoenix
22568 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters157976
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhoenix
2nd rowPhoenix
3rd rowPhoenix
4th rowPhoenix
5th rowPhoenix

Common Values

ValueCountFrequency (%)
Phoenix22568
100.0%

Length

2022-10-20T18:52:22.128440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
maricopa22568
100.0%

Most occurring characters

ValueCountFrequency (%)
a45136
25.0%
M22568
12.5%
r22568
12.5%
i22568
12.5%
c22568
12.5%
o22568
12.5%
p22568
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter157976
87.5%
Uppercase Letter22568
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a45136
28.6%
r22568
14.3%
i22568
14.3%
c22568
14.3%
o22568
14.3%
p22568
14.3%
Uppercase Letter
ValueCountFrequency (%)
M22568
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin180544
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a45136
25.0%
M22568
12.5%
r22568
12.5%
i22568
12.5%
c22568
12.5%
o22568
12.5%
p22568
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII180544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a45136
25.0%
M22568
12.5%
r22568
12.5%
i22568
12.5%
c22568
12.5%
o22568
12.5%
p22568
12.5%

City
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
Phoenix
22568 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters157976
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhoenix
2nd rowPhoenix
3rd rowPhoenix
4th rowPhoenix
5th rowPhoenix

Common Values

ValueCountFrequency (%)
Phoenix22568
100.0%

Length

2022-10-20T19:30:43.134355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:52:22.255309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:30:43.198392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
phoenix22568
100.0%

Most occurring characters

ValueCountFrequency (%)
P22568
14.3%
h22568
14.3%
o22568
14.3%
e22568
14.3%
n22568
14.3%
i22568
14.3%
x22568
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter135408
85.7%
Uppercase Letter22568
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h22568
16.7%
o22568
16.7%
e22568
16.7%
n22568
16.7%
i22568
16.7%
x22568
16.7%
Uppercase Letter
ValueCountFrequency (%)
P22568
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin157976
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P22568
14.3%
h22568
14.3%
o22568
14.3%
e22568
14.3%
n22568
14.3%
i22568
14.3%
x22568
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII157976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P22568
14.3%
h22568
14.3%
o22568
14.3%
e22568
14.3%
n22568
14.3%
i22568
14.3%
x22568
14.3%
Distinct5646
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
Minimum2000-01-01 00:00:00
Maximum2015-12-31 00:00:00
2022-10-20T18:52:22.393661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
phoenix22568
100.0%

Most occurring characters

ValueCountFrequency (%)
P22568
14.3%
h22568
14.3%
o22568
14.3%
e22568
14.3%
n22568
14.3%
i22568
14.3%
x22568
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter135408
85.7%
Uppercase Letter22568
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h22568
16.7%
o22568
16.7%
e22568
16.7%
n22568
16.7%
i22568
16.7%
x22568
16.7%
Uppercase Letter
ValueCountFrequency (%)
P22568
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin157976
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P22568
14.3%
h22568
14.3%
o22568
14.3%
e22568
14.3%
n22568
14.3%
i22568
14.3%
x22568
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII157976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P22568
14.3%
h22568
14.3%
o22568
14.3%
e22568
14.3%
n22568
14.3%
i22568
14.3%
x22568
14.3%
Distinct5646
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
Minimum2000-01-01 00:00:00
Maximum2015-12-31 00:00:00
2022-10-20T19:30:43.268922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:52:22.576392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:30:43.374085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

NO2 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
Parts per billion
22568 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters383656
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion22568
100.0%

Length

2022-10-20T18:52:22.717891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

NO2 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
Parts per billion
22568 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters383656
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion22568
100.0%

Length

2022-10-20T19:30:43.476484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:52:22.838337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:30:43.565400image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts22568
33.3%
per22568
33.3%
billion22568
33.3%

Most occurring characters

ValueCountFrequency (%)
r45136
11.8%
45136
11.8%
i45136
11.8%
l45136
11.8%
P22568
 
5.9%
a22568
 
5.9%
t22568
 
5.9%
s22568
 
5.9%
p22568
 
5.9%
e22568
 
5.9%
Other values (3)67704
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter315952
82.4%
Space Separator45136
 
11.8%
Uppercase Letter22568
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r45136
14.3%
i45136
14.3%
l45136
14.3%
a22568
7.1%
t22568
7.1%
s22568
7.1%
p22568
7.1%
e22568
7.1%
b22568
7.1%
o22568
7.1%
Space Separator
ValueCountFrequency (%)
45136
100.0%
Uppercase Letter
ValueCountFrequency (%)
P22568
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin338520
88.2%
Common45136
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r45136
13.3%
i45136
13.3%
l45136
13.3%
P22568
6.7%
a22568
6.7%
t22568
6.7%
s22568
6.7%
p22568
6.7%
e22568
6.7%
b22568
6.7%
Other values (2)45136
13.3%
Common
ValueCountFrequency (%)
45136
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII383656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r45136
11.8%
45136
11.8%
i45136
11.8%
l45136
11.8%
P22568
 
5.9%
a22568
 
5.9%
t22568
 
5.9%
s22568
 
5.9%
p22568
 
5.9%
e22568
 
5.9%
Other values (3)67704
17.6%

NO2 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct2029
Distinct (%)0.08990606168025522
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean23.423614867777385
Minimum0.52381
Maximum73.285714
Zeros0
Zeros (%)0.0
Memory size180672
2022-10-20T18:52:22.953380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts22568
33.3%
per22568
33.3%
billion22568
33.3%

Most occurring characters

ValueCountFrequency (%)
r45136
11.8%
45136
11.8%
i45136
11.8%
l45136
11.8%
P22568
 
5.9%
a22568
 
5.9%
t22568
 
5.9%
s22568
 
5.9%
p22568
 
5.9%
e22568
 
5.9%
Other values (3)67704
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter315952
82.4%
Space Separator45136
 
11.8%
Uppercase Letter22568
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r45136
14.3%
i45136
14.3%
l45136
14.3%
a22568
7.1%
t22568
7.1%
s22568
7.1%
p22568
7.1%
e22568
7.1%
b22568
7.1%
o22568
7.1%
Space Separator
ValueCountFrequency (%)
45136
100.0%
Uppercase Letter
ValueCountFrequency (%)
P22568
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin338520
88.2%
Common45136
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r45136
13.3%
i45136
13.3%
l45136
13.3%
P22568
6.7%
a22568
6.7%
t22568
6.7%
s22568
6.7%
p22568
6.7%
e22568
6.7%
b22568
6.7%
Other values (2)45136
13.3%
Common
ValueCountFrequency (%)
45136
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII383656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r45136
11.8%
45136
11.8%
i45136
11.8%
l45136
11.8%
P22568
 
5.9%
a22568
 
5.9%
t22568
 
5.9%
s22568
 
5.9%
p22568
 
5.9%
e22568
 
5.9%
Other values (3)67704
17.6%

NO2 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct2029
Distinct (%)0.08990606168025522
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean23.423614867777385
Minimum0.52381
Maximum73.285714
Zeros0
Zeros (%)0.0
Memory size180672
2022-10-20T19:30:43.628367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.52381
5-th percentile8.041667
Q115.541667
median22.952381
Q330.521739
95-th percentile40.375
Maximum73.285714
Range72.761904
Interquartile range (IQR)14.980072

Descriptive statistics

Standard deviation10.14094749
Coefficient of variation (CV)0.4329369119
Kurtosis-0.211470102
Mean23.42361487
Median Absolute Deviation (MAD)7.494048
Skewness0.3007662397
Sum528624.1403
Variance102.8388159
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.613554701 × 10-13
2022-10-20T18:52:23.139626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.52381
5-th percentile8.041667
Q115.541667
median22.952381
Q330.521739
95-th percentile40.375
Maximum73.285714
Range72.761904
Interquartile range (IQR)14.980072

Descriptive statistics

Standard deviation10.14094749
Coefficient of variation (CV)0.4329369119
Kurtosis-0.211470102
Mean23.42361487
Median Absolute Deviation (MAD)7.494048
Skewness0.3007662397
Sum528624.1403
Variance102.8388159
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.613554701 × 10-13
2022-10-20T19:30:43.728876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.12556
 
0.2%
20.87552
 
0.2%
15.58333352
 
0.2%
24.08333352
 
0.2%
22.16666748
 
0.2%
21.2548
 
0.2%
1748
 
0.2%
24.2548
 
0.2%
15.16666748
 
0.2%
27.54166744
 
0.2%
Other values (2019)22072
97.8%
ValueCountFrequency (%)
0.523814
< 0.1%
0.88
< 0.1%
0.8421054
< 0.1%
1.1818184
< 0.1%
1.2708334
< 0.1%
1.3833334
< 0.1%
1.4583334
< 0.1%
1.5541674
< 0.1%
1.7041674
< 0.1%
1.8541674
< 0.1%
ValueCountFrequency (%)
73.2857144
< 0.1%
67.0909094
< 0.1%
66.7916674
< 0.1%
66.5416674
< 0.1%
59.254
< 0.1%
59.0416674
< 0.1%
584
< 0.1%
57.8636364
< 0.1%
57.8333334
< 0.1%
56.0454554
< 0.1%
2022-10-20T18:52:23.678135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.12556
 
0.2%
20.87552
 
0.2%
15.58333352
 
0.2%
24.08333352
 
0.2%
22.16666748
 
0.2%
21.2548
 
0.2%
1748
 
0.2%
24.2548
 
0.2%
15.16666748
 
0.2%
27.54166744
 
0.2%
Other values (2019)22072
97.8%
ValueCountFrequency (%)
0.523814
< 0.1%
0.88
< 0.1%
0.8421054
< 0.1%
1.1818184
< 0.1%
1.2708334
< 0.1%
1.3833334
< 0.1%
1.4583334
< 0.1%
1.5541674
< 0.1%
1.7041674
< 0.1%
1.8541674
< 0.1%
ValueCountFrequency (%)
73.2857144
< 0.1%
67.0909094
< 0.1%
66.7916674
< 0.1%
66.5416674
< 0.1%
59.254
< 0.1%
59.0416674
< 0.1%
584
< 0.1%
57.8636364
< 0.1%
57.8333334
< 0.1%
56.0454554
< 0.1%
2022-10-20T19:30:44.012161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct119
Distinct (%)0.005272952853598015
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean44.74510811768876
Minimum1.9
Maximum124.0
Zeros0
Zeros (%)0.0
Memory size180672
2022-10-20T18:52:24.097961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct119
Distinct (%)0.005272952853598015
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean44.74510811768876
Minimum1.9
Maximum124.0
Zeros0
Zeros (%)0.0
Memory size180672
2022-10-20T19:30:44.151235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile20
Q136
median45
Q354
95-th percentile67
Maximum124
Range122.1
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.18102213
Coefficient of variation (CV)0.3169289947
Kurtosis0.4673504903
Mean44.74510812
Median Absolute Deviation (MAD)9
Skewness-0.08176991405
Sum1009807.6
Variance201.1013888
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.501673973 × 10-19
2022-10-20T18:52:24.307779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile20
Q136
median45
Q354
95-th percentile67
Maximum124
Range122.1
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.18102213
Coefficient of variation (CV)0.3169289947
Kurtosis0.4673504903
Mean44.74510812
Median Absolute Deviation (MAD)9
Skewness-0.08176991405
Sum1009807.6
Variance201.1013888
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.501673973 × 10-19
2022-10-20T19:30:44.256350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45720
 
3.2%
47720
 
3.2%
51704
 
3.1%
50678
 
3.0%
46676
 
3.0%
44676
 
3.0%
48664
 
2.9%
43644
 
2.9%
54642
 
2.8%
49640
 
2.8%
Other values (109)15804
70.0%
ValueCountFrequency (%)
1.94
< 0.1%
24
< 0.1%
34
< 0.1%
3.24
< 0.1%
3.44
< 0.1%
3.74
< 0.1%
3.84
< 0.1%
48
< 0.1%
4.34
< 0.1%
4.64
< 0.1%
ValueCountFrequency (%)
1244
< 0.1%
1174
< 0.1%
1164
< 0.1%
1064
< 0.1%
1018
< 0.1%
954
< 0.1%
944
< 0.1%
924
< 0.1%
894
< 0.1%
884
< 0.1%
2022-10-20T18:52:24.911919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45720
 
3.2%
47720
 
3.2%
51704
 
3.1%
50678
 
3.0%
46676
 
3.0%
44676
 
3.0%
48664
 
2.9%
43644
 
2.9%
54642
 
2.8%
49640
 
2.8%
Other values (109)15804
70.0%
ValueCountFrequency (%)
1.94
< 0.1%
24
< 0.1%
34
< 0.1%
3.24
< 0.1%
3.44
< 0.1%
3.74
< 0.1%
3.84
< 0.1%
48
< 0.1%
4.34
< 0.1%
4.64
< 0.1%
ValueCountFrequency (%)
1244
< 0.1%
1174
< 0.1%
1164
< 0.1%
1064
< 0.1%
1018
< 0.1%
954
< 0.1%
944
< 0.1%
924
< 0.1%
894
< 0.1%
884
< 0.1%
2022-10-20T19:30:44.590354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Hour
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct24
Distinct (%)0.0010634526763559022
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean13.268255937610776
Minimum0
Maximum23
Zeros3220
Zeros (%)0.14267990074441686
Memory size180672
2022-10-20T18:52:25.184460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Hour
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct24
Distinct (%)0.0010634526763559022
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean13.268255937610776
Minimum0
Maximum23
Zeros3220
Zeros (%)0.14267990074441686
Memory size180672
2022-10-20T19:30:44.729394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median19
Q321
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.755133783
Coefficient of variation (CV)0.6598556603
Kurtosis-1.584804431
Mean13.26825594
Median Absolute Deviation (MAD)3
Skewness-0.4069904994
Sum299438
Variance76.65236755
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.616565061 × 10-28
2022-10-20T18:52:25.370852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median19
Q321
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.755133783
Coefficient of variation (CV)0.6598556603
Kurtosis-1.584804431
Mean13.26825594
Median Absolute Deviation (MAD)3
Skewness-0.4069904994
Sum299438
Variance76.65236755
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.616565061 × 10-28
2022-10-20T19:30:44.804922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
03220
14.3%
203168
14.0%
212854
12.6%
222128
9.4%
192108
9.3%
231516
6.7%
61374
6.1%
51252
 
5.5%
18944
 
4.2%
1792
 
3.5%
Other values (14)3212
14.2%
ValueCountFrequency (%)
03220
14.3%
1792
 
3.5%
2368
 
1.6%
3328
 
1.5%
4484
 
2.1%
51252
 
5.5%
61374
6.1%
7724
 
3.2%
8500
 
2.2%
9316
 
1.4%
ValueCountFrequency (%)
231516
6.7%
222128
9.4%
212854
12.6%
203168
14.0%
192108
9.3%
18944
 
4.2%
17140
 
0.6%
1636
 
0.2%
1528
 
0.1%
1416
 
0.1%
2022-10-20T18:52:25.923520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
03220
14.3%
203168
14.0%
212854
12.6%
222128
9.4%
192108
9.3%
231516
6.7%
61374
6.1%
51252
 
5.5%
18944
 
4.2%
1792
 
3.5%
Other values (14)3212
14.2%
ValueCountFrequency (%)
03220
14.3%
1792
 
3.5%
2368
 
1.6%
3328
 
1.5%
4484
 
2.1%
51252
 
5.5%
61374
6.1%
7724
 
3.2%
8500
 
2.2%
9316
 
1.4%
ValueCountFrequency (%)
231516
6.7%
222128
9.4%
212854
12.6%
203168
14.0%
192108
9.3%
18944
 
4.2%
17140
 
0.6%
1636
 
0.2%
1528
 
0.1%
1416
 
0.1%
2022-10-20T19:30:45.142319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 AQI
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct93
Distinct (%)0.004120879120879121
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean42.43335696561503
Minimum1
Maximum105
Zeros0
Zeros (%)0.0
Memory size180672
2022-10-20T18:52:26.180217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 AQI
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct93
Distinct (%)0.004120879120879121
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean42.43335696561503
Minimum1
Maximum105
Zeros0
Zeros (%)0.0
Memory size180672
2022-10-20T19:30:45.274224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19
Q134
median42
Q351
95-th percentile65
Maximum105
Range104
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.76011617
Coefficient of variation (CV)0.3242759272
Kurtosis0.4434632928
Mean42.43335697
Median Absolute Deviation (MAD)9
Skewness0.01362384514
Sum957636
Variance189.3407971
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.052713506 × 10-19
2022-10-20T18:52:26.355012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19
Q134
median42
Q351
95-th percentile65
Maximum105
Range104
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.76011617
Coefficient of variation (CV)0.3242759272
Kurtosis0.4434632928
Mean42.43335697
Median Absolute Deviation (MAD)9
Skewness0.01362384514
Sum957636
Variance189.3407971
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.052713506 × 10-19
2022-10-20T19:30:45.375717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
421396
 
6.2%
44720
 
3.2%
48704
 
3.1%
47678
 
3.0%
43676
 
3.0%
45664
 
2.9%
41644
 
2.9%
51642
 
2.8%
46640
 
2.8%
39612
 
2.7%
Other values (83)15192
67.3%
ValueCountFrequency (%)
14
 
< 0.1%
24
 
< 0.1%
320
 
0.1%
424
 
0.1%
528
0.1%
68
 
< 0.1%
724
 
0.1%
848
0.2%
940
0.2%
1060
0.3%
ValueCountFrequency (%)
1054
 
< 0.1%
1048
< 0.1%
1024
 
< 0.1%
1018
< 0.1%
954
 
< 0.1%
944
 
< 0.1%
914
 
< 0.1%
884
 
< 0.1%
874
 
< 0.1%
8612
0.1%
2022-10-20T18:52:26.847776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
421396
 
6.2%
44720
 
3.2%
48704
 
3.1%
47678
 
3.0%
43676
 
3.0%
45664
 
2.9%
41644
 
2.9%
51642
 
2.8%
46640
 
2.8%
39612
 
2.7%
Other values (83)15192
67.3%
ValueCountFrequency (%)
14
 
< 0.1%
24
 
< 0.1%
320
 
0.1%
424
 
0.1%
528
0.1%
68
 
< 0.1%
724
 
0.1%
848
0.2%
940
0.2%
1060
0.3%
ValueCountFrequency (%)
1054
 
< 0.1%
1048
< 0.1%
1024
 
< 0.1%
1018
< 0.1%
954
 
< 0.1%
944
 
< 0.1%
914
 
< 0.1%
884
 
< 0.1%
874
 
< 0.1%
8612
0.1%
2022-10-20T19:30:45.673997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
Parts per million
22568 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters383656
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million22568
100.0%

Length

2022-10-20T18:52:27.119001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
Parts per million
22568 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters383656
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million22568
100.0%

Length

2022-10-20T19:30:45.800322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:52:27.254326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:30:45.870747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts22568
33.3%
per22568
33.3%
million22568
33.3%

Most occurring characters

ValueCountFrequency (%)
r45136
11.8%
45136
11.8%
i45136
11.8%
l45136
11.8%
P22568
 
5.9%
a22568
 
5.9%
t22568
 
5.9%
s22568
 
5.9%
p22568
 
5.9%
e22568
 
5.9%
Other values (3)67704
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter315952
82.4%
Space Separator45136
 
11.8%
Uppercase Letter22568
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r45136
14.3%
i45136
14.3%
l45136
14.3%
a22568
7.1%
t22568
7.1%
s22568
7.1%
p22568
7.1%
e22568
7.1%
m22568
7.1%
o22568
7.1%
Space Separator
ValueCountFrequency (%)
45136
100.0%
Uppercase Letter
ValueCountFrequency (%)
P22568
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin338520
88.2%
Common45136
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r45136
13.3%
i45136
13.3%
l45136
13.3%
P22568
6.7%
a22568
6.7%
t22568
6.7%
s22568
6.7%
p22568
6.7%
e22568
6.7%
m22568
6.7%
Other values (2)45136
13.3%
Common
ValueCountFrequency (%)
45136
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII383656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r45136
11.8%
45136
11.8%
i45136
11.8%
l45136
11.8%
P22568
 
5.9%
a22568
 
5.9%
t22568
 
5.9%
s22568
 
5.9%
p22568
 
5.9%
e22568
 
5.9%
Other values (3)67704
17.6%

O3 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct1238
Distinct (%)0.054856433888691956
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.023885064516129033
Minimum0.001
Maximum0.063167
Zeros0
Zeros (%)0.0
Memory size180672
2022-10-20T18:52:27.371919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts22568
33.3%
per22568
33.3%
million22568
33.3%

Most occurring characters

ValueCountFrequency (%)
r45136
11.8%
45136
11.8%
i45136
11.8%
l45136
11.8%
P22568
 
5.9%
a22568
 
5.9%
t22568
 
5.9%
s22568
 
5.9%
p22568
 
5.9%
e22568
 
5.9%
Other values (3)67704
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter315952
82.4%
Space Separator45136
 
11.8%
Uppercase Letter22568
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r45136
14.3%
i45136
14.3%
l45136
14.3%
a22568
7.1%
t22568
7.1%
s22568
7.1%
p22568
7.1%
e22568
7.1%
m22568
7.1%
o22568
7.1%
Space Separator
ValueCountFrequency (%)
45136
100.0%
Uppercase Letter
ValueCountFrequency (%)
P22568
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin338520
88.2%
Common45136
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r45136
13.3%
i45136
13.3%
l45136
13.3%
P22568
6.7%
a22568
6.7%
t22568
6.7%
s22568
6.7%
p22568
6.7%
e22568
6.7%
m22568
6.7%
Other values (2)45136
13.3%
Common
ValueCountFrequency (%)
45136
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII383656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r45136
11.8%
45136
11.8%
i45136
11.8%
l45136
11.8%
P22568
 
5.9%
a22568
 
5.9%
t22568
 
5.9%
s22568
 
5.9%
p22568
 
5.9%
e22568
 
5.9%
Other values (3)67704
17.6%

O3 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct1238
Distinct (%)0.054856433888691956
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.023885064516129033
Minimum0.001
Maximum0.063167
Zeros0
Zeros (%)0.0
Memory size180672
2022-10-20T19:30:45.931480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.006875
Q10.014625
median0.023542
Q30.032208
95-th percentile0.043125
Maximum0.063167
Range0.062167
Interquartile range (IQR)0.017583

Descriptive statistics

Standard deviation0.01131877649
Coefficient of variation (CV)0.4738851129
Kurtosis-0.6612348643
Mean0.02388506452
Median Absolute Deviation (MAD)0.008833
Skewness0.2438155849
Sum539.038136
Variance0.0001281147013
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.201841185 × 10-7
2022-10-20T18:52:27.563953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.006875
Q10.014625
median0.023542
Q30.032208
95-th percentile0.043125
Maximum0.063167
Range0.062167
Interquartile range (IQR)0.017583

Descriptive statistics

Standard deviation0.01131877649
Coefficient of variation (CV)0.4738851129
Kurtosis-0.6612348643
Mean0.02388506452
Median Absolute Deviation (MAD)0.008833
Skewness0.2438155849
Sum539.038136
Variance0.0001281147013
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.201841185 × 10-7
2022-10-20T19:30:46.025195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02937576
 
0.3%
0.01545860
 
0.3%
0.01462560
 
0.3%
0.02354256
 
0.2%
0.02862556
 
0.2%
0.02495856
 
0.2%
0.01166752
 
0.2%
0.02933352
 
0.2%
0.03058352
 
0.2%
0.00908352
 
0.2%
Other values (1228)21996
97.5%
ValueCountFrequency (%)
0.0018
< 0.1%
0.0015454
< 0.1%
0.0016114
< 0.1%
0.0016678
< 0.1%
0.001754
< 0.1%
0.0018334
< 0.1%
0.0019174
< 0.1%
0.0028
< 0.1%
0.0020424
< 0.1%
0.0023334
< 0.1%
ValueCountFrequency (%)
0.0631674
< 0.1%
0.0583334
< 0.1%
0.0566254
< 0.1%
0.0563754
< 0.1%
0.0563334
< 0.1%
0.0558334
< 0.1%
0.0557084
< 0.1%
0.0550834
< 0.1%
0.0550424
< 0.1%
0.0554
< 0.1%
2022-10-20T18:52:28.041034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02937576
 
0.3%
0.01545860
 
0.3%
0.01462560
 
0.3%
0.02354256
 
0.2%
0.02862556
 
0.2%
0.02495856
 
0.2%
0.01166752
 
0.2%
0.02933352
 
0.2%
0.03058352
 
0.2%
0.00908352
 
0.2%
Other values (1228)21996
97.5%
ValueCountFrequency (%)
0.0018
< 0.1%
0.0015454
< 0.1%
0.0016114
< 0.1%
0.0016678
< 0.1%
0.001754
< 0.1%
0.0018334
< 0.1%
0.0019174
< 0.1%
0.0028
< 0.1%
0.0020424
< 0.1%
0.0023334
< 0.1%
ValueCountFrequency (%)
0.0631674
< 0.1%
0.0583334
< 0.1%
0.0566254
< 0.1%
0.0563754
< 0.1%
0.0563334
< 0.1%
0.0558334
< 0.1%
0.0557084
< 0.1%
0.0550834
< 0.1%
0.0550424
< 0.1%
0.0554
< 0.1%
2022-10-20T19:30:46.236477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct86
Distinct (%)0.0038107054236086496
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.04314906061680255
Minimum0.001
Maximum0.089
Zeros0
Zeros (%)0.0
Memory size180672
2022-10-20T18:52:28.292936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct86
Distinct (%)0.0038107054236086496
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.04314906061680255
Minimum0.001
Maximum0.089
Zeros0
Zeros (%)0.0
Memory size180672
2022-10-20T19:30:46.367961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.016
Q10.033
median0.044
Q30.054
95-th percentile0.066
Maximum0.089
Range0.088
Interquartile range (IQR)0.021

Descriptive statistics

Standard deviation0.01528137384
Coefficient of variation (CV)0.354153106
Kurtosis-0.4324198949
Mean0.04314906062
Median Absolute Deviation (MAD)0.011
Skewness-0.224903219
Sum973.788
Variance0.0002335203864
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.273232853 × 10-7
2022-10-20T18:52:28.459094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.016
Q10.033
median0.044
Q30.054
95-th percentile0.066
Maximum0.089
Range0.088
Interquartile range (IQR)0.021

Descriptive statistics

Standard deviation0.01528137384
Coefficient of variation (CV)0.354153106
Kurtosis-0.4324198949
Mean0.04314906062
Median Absolute Deviation (MAD)0.011
Skewness-0.224903219
Sum973.788
Variance0.0002335203864
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.273232853 × 10-7
2022-10-20T19:30:46.466745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.048630
 
2.8%
0.054592
 
2.6%
0.044588
 
2.6%
0.047584
 
2.6%
0.053580
 
2.6%
0.05576
 
2.6%
0.04576
 
2.6%
0.051576
 
2.6%
0.046568
 
2.5%
0.049568
 
2.5%
Other values (76)16730
74.1%
ValueCountFrequency (%)
0.0018
 
< 0.1%
0.0024
 
< 0.1%
0.00336
0.2%
0.00416
 
0.1%
0.00532
 
0.1%
0.00644
0.2%
0.00748
0.2%
0.00884
0.4%
0.00980
0.4%
0.0188
0.4%
ValueCountFrequency (%)
0.0894
 
< 0.1%
0.0884
 
< 0.1%
0.0848
 
< 0.1%
0.0838
 
< 0.1%
0.0828
 
< 0.1%
0.08120
0.1%
0.0812
 
0.1%
0.07920
0.1%
0.07828
0.1%
0.07736
0.2%
2022-10-20T18:52:29.116490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.048630
 
2.8%
0.054592
 
2.6%
0.044588
 
2.6%
0.047584
 
2.6%
0.053580
 
2.6%
0.05576
 
2.6%
0.04576
 
2.6%
0.051576
 
2.6%
0.046568
 
2.5%
0.049568
 
2.5%
Other values (76)16730
74.1%
ValueCountFrequency (%)
0.0018
 
< 0.1%
0.0024
 
< 0.1%
0.00336
0.2%
0.00416
 
0.1%
0.00532
 
0.1%
0.00644
0.2%
0.00748
0.2%
0.00884
0.4%
0.00980
0.4%
0.0188
0.4%
ValueCountFrequency (%)
0.0894
 
< 0.1%
0.0884
 
< 0.1%
0.0848
 
< 0.1%
0.0838
 
< 0.1%
0.0828
 
< 0.1%
0.08120
0.1%
0.0812
 
0.1%
0.07920
0.1%
0.07828
0.1%
0.07736
0.2%
2022-10-20T19:30:46.732078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Hour
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct22
Distinct (%)0.0009748316199929103
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean10.309110244594116
Minimum0
Maximum23
Zeros128
Zeros (%)0.005671747607231478
Memory size180672
2022-10-20T18:52:29.405388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Hour
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct22
Distinct (%)0.0009748316199929103
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean10.309110244594116
Minimum0
Maximum23
Zeros128
Zeros (%)0.005671747607231478
Memory size180672
2022-10-20T19:30:46.862989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q110
median10
Q311
95-th percentile12
Maximum23
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.893420762
Coefficient of variation (CV)0.1836648088
Kurtosis20.39009329
Mean10.30911024
Median Absolute Deviation (MAD)1
Skewness1.969151302
Sum232656
Variance3.585042183
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.405028932 × 10-29
2022-10-20T18:52:29.560114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q110
median10
Q311
95-th percentile12
Maximum23
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.893420762
Coefficient of variation (CV)0.1836648088
Kurtosis20.39009329
Mean10.30911024
Median Absolute Deviation (MAD)1
Skewness1.969151302
Sum232656
Variance3.585042183
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.405028932 × 10-29
2022-10-20T19:30:46.946971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
109450
41.9%
116420
28.4%
93972
17.6%
121018
 
4.5%
8612
 
2.7%
13236
 
1.0%
0128
 
0.6%
14116
 
0.5%
7104
 
0.5%
2380
 
0.4%
Other values (12)432
 
1.9%
ValueCountFrequency (%)
0128
 
0.6%
112
 
0.1%
316
 
0.1%
54
 
< 0.1%
620
 
0.1%
7104
 
0.5%
8612
 
2.7%
93972
17.6%
109450
41.9%
116420
28.4%
ValueCountFrequency (%)
2380
0.4%
2260
0.3%
2160
0.3%
2068
0.3%
1952
0.2%
1836
 
0.2%
1720
 
0.1%
1640
 
0.2%
1544
 
0.2%
14116
0.5%
2022-10-20T18:52:30.117306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
109450
41.9%
116420
28.4%
93972
17.6%
121018
 
4.5%
8612
 
2.7%
13236
 
1.0%
0128
 
0.6%
14116
 
0.5%
7104
 
0.5%
2380
 
0.4%
Other values (12)432
 
1.9%
ValueCountFrequency (%)
0128
 
0.6%
112
 
0.1%
316
 
0.1%
54
 
< 0.1%
620
 
0.1%
7104
 
0.5%
8612
 
2.7%
93972
17.6%
109450
41.9%
116420
28.4%
ValueCountFrequency (%)
2380
0.4%
2260
0.3%
2160
0.3%
2068
0.3%
1952
0.2%
1836
 
0.2%
1720
 
0.1%
1640
 
0.2%
1544
 
0.2%
14116
0.5%
2022-10-20T19:30:47.255042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 AQI
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct82
Distinct (%)0.003633463310882666
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean39.55680609712868
Minimum1
Maximum135
Zeros0
Zeros (%)0.0
Memory size180672
2022-10-20T18:52:30.384182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 AQI
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct82
Distinct (%)0.003633463310882666
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean39.55680609712868
Minimum1
Maximum135
Zeros0
Zeros (%)0.0
Memory size180672
2022-10-20T19:30:47.391037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q128
median38
Q347
95-th percentile77
Maximum135
Range134
Interquartile range (IQR)19

Descriptive statistics

Standard deviation18.19625021
Coefficient of variation (CV)0.4600030186
Kurtosis2.490109832
Mean39.5568061
Median Absolute Deviation (MAD)9
Skewness1.140067925
Sum892718
Variance331.1035217
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.060826789 × 10-11
2022-10-20T18:52:30.569613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q128
median38
Q347
95-th percentile77
Maximum135
Range134
Interquartile range (IQR)19

Descriptive statistics

Standard deviation18.19625021
Coefficient of variation (CV)0.4600030186
Kurtosis2.490109832
Mean39.5568061
Median Absolute Deviation (MAD)9
Skewness1.140067925
Sum892718
Variance331.1035217
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.060826789 × 10-11
2022-10-20T19:30:47.510183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
421092
 
4.8%
47934
 
4.1%
36908
 
4.0%
31854
 
3.8%
44686
 
3.0%
41626
 
2.8%
37604
 
2.7%
46596
 
2.6%
25596
 
2.6%
40576
 
2.6%
Other values (72)15096
66.9%
ValueCountFrequency (%)
18
 
< 0.1%
24
 
< 0.1%
352
 
0.2%
432
 
0.1%
544
 
0.2%
648
 
0.2%
784
0.4%
8156
0.7%
9100
0.4%
1092
0.4%
ValueCountFrequency (%)
1354
 
< 0.1%
1324
 
< 0.1%
1298
 
< 0.1%
12216
0.1%
1198
 
< 0.1%
1168
 
< 0.1%
11512
0.1%
11420
0.1%
1124
 
< 0.1%
11112
0.1%
2022-10-20T18:52:31.176487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
421092
 
4.8%
47934
 
4.1%
36908
 
4.0%
31854
 
3.8%
44686
 
3.0%
41626
 
2.8%
37604
 
2.7%
46596
 
2.6%
25596
 
2.6%
40576
 
2.6%
Other values (72)15096
66.9%
ValueCountFrequency (%)
18
 
< 0.1%
24
 
< 0.1%
352
 
0.2%
432
 
0.1%
544
 
0.2%
648
 
0.2%
784
0.4%
8156
0.7%
9100
0.4%
1092
0.4%
ValueCountFrequency (%)
1354
 
< 0.1%
1324
 
< 0.1%
1298
 
< 0.1%
12216
0.1%
1198
 
< 0.1%
1168
 
< 0.1%
11512
0.1%
11420
0.1%
1124
 
< 0.1%
11112
0.1%
2022-10-20T19:30:47.740700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
Parts per billion
22568 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters383656
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion22568
100.0%

Length

2022-10-20T18:52:31.438751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
Parts per billion
22568 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters383656
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion22568
100.0%

Length

2022-10-20T19:30:47.866816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:52:31.562990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:30:47.942872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts22568
33.3%
per22568
33.3%
billion22568
33.3%

Most occurring characters

ValueCountFrequency (%)
r45136
11.8%
45136
11.8%
i45136
11.8%
l45136
11.8%
P22568
 
5.9%
a22568
 
5.9%
t22568
 
5.9%
s22568
 
5.9%
p22568
 
5.9%
e22568
 
5.9%
Other values (3)67704
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter315952
82.4%
Space Separator45136
 
11.8%
Uppercase Letter22568
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r45136
14.3%
i45136
14.3%
l45136
14.3%
a22568
7.1%
t22568
7.1%
s22568
7.1%
p22568
7.1%
e22568
7.1%
b22568
7.1%
o22568
7.1%
Space Separator
ValueCountFrequency (%)
45136
100.0%
Uppercase Letter
ValueCountFrequency (%)
P22568
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin338520
88.2%
Common45136
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r45136
13.3%
i45136
13.3%
l45136
13.3%
P22568
6.7%
a22568
6.7%
t22568
6.7%
s22568
6.7%
p22568
6.7%
e22568
6.7%
b22568
6.7%
Other values (2)45136
13.3%
Common
ValueCountFrequency (%)
45136
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII383656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r45136
11.8%
45136
11.8%
i45136
11.8%
l45136
11.8%
P22568
 
5.9%
a22568
 
5.9%
t22568
 
5.9%
s22568
 
5.9%
p22568
 
5.9%
e22568
 
5.9%
Other values (3)67704
17.6%

SO2 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct1317
Distinct (%)0.05835696561503013
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean1.7070794210829492
Minimum0.0
Maximum12.166667
Zeros708
Zeros (%)0.03137185395249911
Memory size180672
2022-10-20T18:52:31.691265image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts22568
33.3%
per22568
33.3%
billion22568
33.3%

Most occurring characters

ValueCountFrequency (%)
r45136
11.8%
45136
11.8%
i45136
11.8%
l45136
11.8%
P22568
 
5.9%
a22568
 
5.9%
t22568
 
5.9%
s22568
 
5.9%
p22568
 
5.9%
e22568
 
5.9%
Other values (3)67704
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter315952
82.4%
Space Separator45136
 
11.8%
Uppercase Letter22568
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r45136
14.3%
i45136
14.3%
l45136
14.3%
a22568
7.1%
t22568
7.1%
s22568
7.1%
p22568
7.1%
e22568
7.1%
b22568
7.1%
o22568
7.1%
Space Separator
ValueCountFrequency (%)
45136
100.0%
Uppercase Letter
ValueCountFrequency (%)
P22568
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin338520
88.2%
Common45136
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r45136
13.3%
i45136
13.3%
l45136
13.3%
P22568
6.7%
a22568
6.7%
t22568
6.7%
s22568
6.7%
p22568
6.7%
e22568
6.7%
b22568
6.7%
Other values (2)45136
13.3%
Common
ValueCountFrequency (%)
45136
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII383656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r45136
11.8%
45136
11.8%
i45136
11.8%
l45136
11.8%
P22568
 
5.9%
a22568
 
5.9%
t22568
 
5.9%
s22568
 
5.9%
p22568
 
5.9%
e22568
 
5.9%
Other values (3)67704
17.6%

SO2 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct1317
Distinct (%)0.05835696561503013
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean1.7070794210829492
Minimum0.0
Maximum12.166667
Zeros708
Zeros (%)0.03137185395249911
Memory size180672
2022-10-20T19:30:48.003677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.075
Q10.875
median1.325
Q32.25
95-th percentile4.391304
Maximum12.166667
Range12.166667
Interquartile range (IQR)1.375

Descriptive statistics

Standard deviation1.403876829
Coefficient of variation (CV)0.8223851869
Kurtosis6.370731432
Mean1.707079421
Median Absolute Deviation (MAD)0.675
Skewness1.954403355
Sum38525.36837
Variance1.97087015
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value8.147275733 × 10-18
2022-10-20T18:52:31.874702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.075
Q10.875
median1.325
Q32.25
95-th percentile4.391304
Maximum12.166667
Range12.166667
Interquartile range (IQR)1.375

Descriptive statistics

Standard deviation1.403876829
Coefficient of variation (CV)0.8223851869
Kurtosis6.370731432
Mean1.707079421
Median Absolute Deviation (MAD)0.675
Skewness1.954403355
Sum38525.36837
Variance1.97087015
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value8.147275733 × 10-18
2022-10-20T19:30:48.098753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11054
 
4.7%
0708
 
3.1%
1.083333262
 
1.2%
1.125236
 
1.0%
2222
 
1.0%
1.25222
 
1.0%
1.041667214
 
0.9%
1.075198
 
0.9%
1.5196
 
0.9%
1.166667178
 
0.8%
Other values (1307)19078
84.5%
ValueCountFrequency (%)
0708
3.1%
0.0375110
 
0.5%
0.041667110
 
0.5%
0.04285738
 
0.2%
0.04347824
 
0.1%
0.04545510
 
< 0.1%
0.0476194
 
< 0.1%
0.0514
 
0.1%
0.0526322
 
< 0.1%
0.0555562
 
< 0.1%
ValueCountFrequency (%)
12.1666672
< 0.1%
12.1252
< 0.1%
12.0833332
< 0.1%
12.052
< 0.1%
11.7666672
< 0.1%
11.6254
< 0.1%
11.62
< 0.1%
11.58752
< 0.1%
10.9523812
< 0.1%
10.9166672
< 0.1%
2022-10-20T18:52:32.366383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11054
 
4.7%
0708
 
3.1%
1.083333262
 
1.2%
1.125236
 
1.0%
2222
 
1.0%
1.25222
 
1.0%
1.041667214
 
0.9%
1.075198
 
0.9%
1.5196
 
0.9%
1.166667178
 
0.8%
Other values (1307)19078
84.5%
ValueCountFrequency (%)
0708
3.1%
0.0375110
 
0.5%
0.041667110
 
0.5%
0.04285738
 
0.2%
0.04347824
 
0.1%
0.04545510
 
< 0.1%
0.0476194
 
< 0.1%
0.0514
 
0.1%
0.0526322
 
< 0.1%
0.0555562
 
< 0.1%
ValueCountFrequency (%)
12.1666672
< 0.1%
12.1252
< 0.1%
12.0833332
< 0.1%
12.052
< 0.1%
11.7666672
< 0.1%
11.6254
< 0.1%
11.62
< 0.1%
11.58752
< 0.1%
10.9523812
< 0.1%
10.9166672
< 0.1%
2022-10-20T19:30:48.445822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct87
Distinct (%)0.0038550159517901452
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean3.6576391350584903
Minimum0.0
Maximum69.0
Zeros708
Zeros (%)0.03137185395249911
Memory size180672
2022-10-20T18:52:32.635917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct87
Distinct (%)0.0038550159517901452
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean3.6576391350584903
Minimum0.0
Maximum69.0
Zeros708
Zeros (%)0.03137185395249911
Memory size180672
2022-10-20T19:30:48.577838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q11.6
median3
Q35
95-th percentile10
Maximum69
Range69
Interquartile range (IQR)3.4

Descriptive statistics

Standard deviation3.102048512
Coefficient of variation (CV)0.8481013017
Kurtosis24.46710674
Mean3.657639135
Median Absolute Deviation (MAD)1.6
Skewness2.772268573
Sum82545.6
Variance9.622704968
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.796441733 × 10-16
2022-10-20T18:52:33.010743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q11.6
median3
Q35
95-th percentile10
Maximum69
Range69
Interquartile range (IQR)3.4

Descriptive statistics

Standard deviation3.102048512
Coefficient of variation (CV)0.8481013017
Kurtosis24.46710674
Mean3.657639135
Median Absolute Deviation (MAD)1.6
Skewness2.772268573
Sum82545.6
Variance9.622704968
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.796441733 × 10-16
2022-10-20T19:30:48.683919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23443
15.3%
12904
12.9%
32408
 
10.7%
41824
 
8.1%
51284
 
5.7%
6929
 
4.1%
1.3880
 
3.9%
1.6850
 
3.8%
2.3717
 
3.2%
0708
 
3.1%
Other values (77)6621
29.3%
ValueCountFrequency (%)
0708
3.1%
0.12
 
< 0.1%
0.24
 
< 0.1%
0.3266
 
1.2%
0.412
 
0.1%
0.512
 
0.1%
0.6250
 
1.1%
0.74
 
< 0.1%
0.812
 
0.1%
0.910
 
< 0.1%
ValueCountFrequency (%)
692
 
< 0.1%
422
 
< 0.1%
292
 
< 0.1%
282
 
< 0.1%
262
 
< 0.1%
25.62
 
< 0.1%
24.32
 
< 0.1%
246
< 0.1%
234
< 0.1%
22.62
 
< 0.1%
2022-10-20T18:52:33.701044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23443
15.3%
12904
12.9%
32408
 
10.7%
41824
 
8.1%
51284
 
5.7%
6929
 
4.1%
1.3880
 
3.9%
1.6850
 
3.8%
2.3717
 
3.2%
0708
 
3.1%
Other values (77)6621
29.3%
ValueCountFrequency (%)
0708
3.1%
0.12
 
< 0.1%
0.24
 
< 0.1%
0.3266
 
1.2%
0.412
 
0.1%
0.512
 
0.1%
0.6250
 
1.1%
0.74
 
< 0.1%
0.812
 
0.1%
0.910
 
< 0.1%
ValueCountFrequency (%)
692
 
< 0.1%
422
 
< 0.1%
292
 
< 0.1%
282
 
< 0.1%
262
 
< 0.1%
25.62
 
< 0.1%
24.32
 
< 0.1%
246
< 0.1%
234
< 0.1%
22.62
 
< 0.1%
2022-10-20T19:30:48.938986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Hour
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct24
Distinct (%)0.0010634526763559022
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean10.21774193548387
Minimum0
Maximum23
Zeros2732
Zeros (%)0.12105636299184687
Memory size180672
2022-10-20T18:52:33.963572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Hour
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct24
Distinct (%)0.0010634526763559022
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean10.21774193548387
Minimum0
Maximum23
Zeros2732
Zeros (%)0.12105636299184687
Memory size180672
2022-10-20T19:30:49.069097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q320
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)18

Descriptive statistics

Standard deviation8.40625021
Coefficient of variation (CV)0.8227111492
Kurtosis-1.303880582
Mean10.21774194
Median Absolute Deviation (MAD)6
Skewness0.4618711657
Sum230594
Variance70.66504259
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value9.647924471 × 10-29
2022-10-20T18:52:34.119962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q320
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)18

Descriptive statistics

Standard deviation8.40625021
Coefficient of variation (CV)0.8227111492
Kurtosis-1.303880582
Mean10.21774194
Median Absolute Deviation (MAD)6
Skewness0.4618711657
Sum230594
Variance70.66504259
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value9.647924471 × 10-29
2022-10-20T19:30:49.154221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
233919
17.4%
83056
13.5%
23002
13.3%
02732
12.1%
51682
7.5%
111373
 
6.1%
22936
 
4.1%
20842
 
3.7%
7842
 
3.7%
6820
 
3.6%
Other values (14)3364
14.9%
ValueCountFrequency (%)
02732
12.1%
1426
 
1.9%
23002
13.3%
3230
 
1.0%
4272
 
1.2%
51682
7.5%
6820
 
3.6%
7842
 
3.7%
83056
13.5%
9581
 
2.6%
ValueCountFrequency (%)
233919
17.4%
22936
 
4.1%
21757
 
3.4%
20842
 
3.7%
19150
 
0.7%
1824
 
0.1%
17102
 
0.5%
1616
 
0.1%
1518
 
0.1%
14275
 
1.2%
2022-10-20T18:52:34.634057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
233919
17.4%
83056
13.5%
23002
13.3%
02732
12.1%
51682
7.5%
111373
 
6.1%
22936
 
4.1%
20842
 
3.7%
7842
 
3.7%
6820
 
3.6%
Other values (14)3364
14.9%
ValueCountFrequency (%)
02732
12.1%
1426
 
1.9%
23002
13.3%
3230
 
1.0%
4272
 
1.2%
51682
7.5%
6820
 
3.6%
7842
 
3.7%
83056
13.5%
9581
 
2.6%
ValueCountFrequency (%)
233919
17.4%
22936
 
4.1%
21757
 
3.4%
20842
 
3.7%
19150
 
0.7%
1824
 
0.1%
17102
 
0.5%
1616
 
0.1%
1518
 
0.1%
14275
 
1.2%
2022-10-20T19:30:49.895967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 AQI
Numeric time series

HIGH CORRELATION
MISSING
NON STATIONARY
SEASONAL
ZEROS

Distinct30
Distinct (%)0.0026579250465136885
Missing11281
Missing (%)0.49986706841545553
Infinite0
Infinite (%)0.0
Mean5.822893594400638
Minimum0.0
Maximum92.0
Zeros384
Zeros (%)0.017015242821694435
Memory size180672
2022-10-20T18:52:35.467315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 AQI
Numeric time series

HIGH CORRELATION
MISSING
NON STATIONARY
SEASONAL
ZEROS

Distinct30
Distinct (%)0.0026579250465136885
Missing11281
Missing (%)0.49986706841545553
Infinite0
Infinite (%)0.0
Mean5.822893594400638
Minimum0.0
Maximum92.0
Zeros384
Zeros (%)0.017015242821694435
Memory size180672
2022-10-20T19:30:50.025290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q37
95-th percentile16
Maximum92
Range92
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.906145216
Coefficient of variation (CV)0.842561372
Kurtosis22.8263953
Mean5.822893594
Median Absolute Deviation (MAD)3
Skewness2.69542901
Sum65723
Variance24.07026088
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value6.377439342 × 10-12
2022-10-20T18:52:35.609902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q37
95-th percentile16
Maximum92
Range92
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.906145216
Coefficient of variation (CV)0.842561372
Kurtosis22.8263953
Mean5.822893594
Median Absolute Deviation (MAD)3
Skewness2.69542901
Sum65723
Variance24.07026088
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value6.377439342 × 10-12
2022-10-20T19:30:50.102265image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
32380
 
10.5%
41794
 
7.9%
11614
 
7.2%
61362
 
6.0%
71012
 
4.5%
9731
 
3.2%
10512
 
2.3%
11402
 
1.8%
0384
 
1.7%
13302
 
1.3%
Other values (20)794
 
3.5%
(Missing)11281
50.0%
ValueCountFrequency (%)
0384
 
1.7%
11614
7.2%
32380
10.5%
41794
7.9%
61362
6.0%
71012
4.5%
9731
 
3.2%
10512
 
2.3%
11402
 
1.8%
13302
 
1.3%
ValueCountFrequency (%)
922
 
< 0.1%
592
 
< 0.1%
412
 
< 0.1%
402
 
< 0.1%
372
 
< 0.1%
346
< 0.1%
334
< 0.1%
316
< 0.1%
308
< 0.1%
292
 
< 0.1%
2022-10-20T18:52:35.932869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
32380
 
10.5%
41794
 
7.9%
11614
 
7.2%
61362
 
6.0%
71012
 
4.5%
9731
 
3.2%
10512
 
2.3%
11402
 
1.8%
0384
 
1.7%
13302
 
1.3%
Other values (20)794
 
3.5%
(Missing)11281
50.0%
ValueCountFrequency (%)
0384
 
1.7%
11614
7.2%
32380
10.5%
41794
7.9%
61362
6.0%
71012
4.5%
9731
 
3.2%
10512
 
2.3%
11402
 
1.8%
13302
 
1.3%
ValueCountFrequency (%)
922
 
< 0.1%
592
 
< 0.1%
412
 
< 0.1%
402
 
< 0.1%
372
 
< 0.1%
346
< 0.1%
334
< 0.1%
316
< 0.1%
308
< 0.1%
292
 
< 0.1%
2022-10-20T19:30:50.243133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

CO Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
Parts per million
22568 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters383656
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million22568
100.0%

Length

2022-10-20T18:52:36.174133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

CO Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.4 KiB
Parts per million
22568 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters383656
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million22568
100.0%

Length

2022-10-20T19:30:50.377335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:52:36.304792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:30:50.446962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts22568
33.3%
per22568
33.3%
million22568
33.3%

Most occurring characters

ValueCountFrequency (%)
r45136
11.8%
45136
11.8%
i45136
11.8%
l45136
11.8%
P22568
 
5.9%
a22568
 
5.9%
t22568
 
5.9%
s22568
 
5.9%
p22568
 
5.9%
e22568
 
5.9%
Other values (3)67704
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter315952
82.4%
Space Separator45136
 
11.8%
Uppercase Letter22568
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r45136
14.3%
i45136
14.3%
l45136
14.3%
a22568
7.1%
t22568
7.1%
s22568
7.1%
p22568
7.1%
e22568
7.1%
m22568
7.1%
o22568
7.1%
Space Separator
ValueCountFrequency (%)
45136
100.0%
Uppercase Letter
ValueCountFrequency (%)
P22568
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin338520
88.2%
Common45136
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r45136
13.3%
i45136
13.3%
l45136
13.3%
P22568
6.7%
a22568
6.7%
t22568
6.7%
s22568
6.7%
p22568
6.7%
e22568
6.7%
m22568
6.7%
Other values (2)45136
13.3%
Common
ValueCountFrequency (%)
45136
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII383656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r45136
11.8%
45136
11.8%
i45136
11.8%
l45136
11.8%
P22568
 
5.9%
a22568
 
5.9%
t22568
 
5.9%
s22568
 
5.9%
p22568
 
5.9%
e22568
 
5.9%
Other values (3)67704
17.6%

CO Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct1202
Distinct (%)0.0532612548741581
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.5812408964019852
Minimum0.0
Maximum3.575
Zeros28
Zeros (%)0.0012406947890818859
Memory size180672
2022-10-20T18:52:36.446029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts22568
33.3%
per22568
33.3%
million22568
33.3%

Most occurring characters

ValueCountFrequency (%)
r45136
11.8%
45136
11.8%
i45136
11.8%
l45136
11.8%
P22568
 
5.9%
a22568
 
5.9%
t22568
 
5.9%
s22568
 
5.9%
p22568
 
5.9%
e22568
 
5.9%
Other values (3)67704
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter315952
82.4%
Space Separator45136
 
11.8%
Uppercase Letter22568
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r45136
14.3%
i45136
14.3%
l45136
14.3%
a22568
7.1%
t22568
7.1%
s22568
7.1%
p22568
7.1%
e22568
7.1%
m22568
7.1%
o22568
7.1%
Space Separator
ValueCountFrequency (%)
45136
100.0%
Uppercase Letter
ValueCountFrequency (%)
P22568
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin338520
88.2%
Common45136
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r45136
13.3%
i45136
13.3%
l45136
13.3%
P22568
6.7%
a22568
6.7%
t22568
6.7%
s22568
6.7%
p22568
6.7%
e22568
6.7%
m22568
6.7%
Other values (2)45136
13.3%
Common
ValueCountFrequency (%)
45136
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII383656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r45136
11.8%
45136
11.8%
i45136
11.8%
l45136
11.8%
P22568
 
5.9%
a22568
 
5.9%
t22568
 
5.9%
s22568
 
5.9%
p22568
 
5.9%
e22568
 
5.9%
Other values (3)67704
17.6%

CO Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct1202
Distinct (%)0.0532612548741581
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.5812408964019852
Minimum0.0
Maximum3.575
Zeros28
Zeros (%)0.0012406947890818859
Memory size180672
2022-10-20T19:30:50.510202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.145833
Q10.2875
median0.454167
Q30.75
95-th percentile1.475
Maximum3.575
Range3.575
Interquartile range (IQR)0.4625

Descriptive statistics

Standard deviation0.4256151976
Coefficient of variation (CV)0.7322526689
Kurtosis3.655770076
Mean0.5812408964
Median Absolute Deviation (MAD)0.204167
Skewness1.709631641
Sum13117.44455
Variance0.1811482965
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.097256943 × 10-10
2022-10-20T18:52:36.627632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.145833
Q10.2875
median0.454167
Q30.75
95-th percentile1.475
Maximum3.575
Range3.575
Interquartile range (IQR)0.4625

Descriptive statistics

Standard deviation0.4256151976
Coefficient of variation (CV)0.7322526689
Kurtosis3.655770076
Mean0.5812408964
Median Absolute Deviation (MAD)0.204167
Skewness1.709631641
Sum13117.44455
Variance0.1811482965
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.097256943 × 10-10
2022-10-20T19:30:50.606020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3192
 
0.9%
0.25178
 
0.8%
0.329167174
 
0.8%
0.2625172
 
0.8%
0.2168
 
0.7%
0.316667168
 
0.7%
0.270833160
 
0.7%
0.233333160
 
0.7%
0.308333156
 
0.7%
0.366667154
 
0.7%
Other values (1192)20886
92.5%
ValueCountFrequency (%)
028
0.1%
0.0041676
 
< 0.1%
0.0083338
 
< 0.1%
0.01252
 
< 0.1%
0.01666722
0.1%
0.0181822
 
< 0.1%
0.022
 
< 0.1%
0.02083312
0.1%
0.0214292
 
< 0.1%
0.0217392
 
< 0.1%
ValueCountFrequency (%)
3.5752
< 0.1%
3.1363642
< 0.1%
3.01252
< 0.1%
2.9916672
< 0.1%
2.9583332
< 0.1%
2.8916672
< 0.1%
2.88752
< 0.1%
2.8130432
< 0.1%
2.78752
< 0.1%
2.7708332
< 0.1%
2022-10-20T18:52:37.100036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3192
 
0.9%
0.25178
 
0.8%
0.329167174
 
0.8%
0.2625172
 
0.8%
0.2168
 
0.7%
0.316667168
 
0.7%
0.270833160
 
0.7%
0.233333160
 
0.7%
0.308333156
 
0.7%
0.366667154
 
0.7%
Other values (1192)20886
92.5%
ValueCountFrequency (%)
028
0.1%
0.0041676
 
< 0.1%
0.0083338
 
< 0.1%
0.01252
 
< 0.1%
0.01666722
0.1%
0.0181822
 
< 0.1%
0.022
 
< 0.1%
0.02083312
0.1%
0.0214292
 
< 0.1%
0.0217392
 
< 0.1%
ValueCountFrequency (%)
3.5752
< 0.1%
3.1363642
< 0.1%
3.01252
< 0.1%
2.9916672
< 0.1%
2.9583332
< 0.1%
2.8916672
< 0.1%
2.88752
< 0.1%
2.8130432
< 0.1%
2.78752
< 0.1%
2.7708332
< 0.1%
2022-10-20T19:30:50.867973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

CO 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct72
Distinct (%)0.0031903580290677065
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean1.2530397022332505
Minimum0.0
Maximum8.1
Zeros28
Zeros (%)0.0012406947890818859
Memory size180672
2022-10-20T18:52:37.354604image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

CO 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct72
Distinct (%)0.0031903580290677065
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean1.2530397022332505
Minimum0.0
Maximum8.1
Zeros28
Zeros (%)0.0012406947890818859
Memory size180672
2022-10-20T19:30:51.009465image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q10.5
median1
Q31.7
95-th percentile3.3
Maximum8.1
Range8.1
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.010290727
Coefficient of variation (CV)0.80627192
Kurtosis3.604120291
Mean1.253039702
Median Absolute Deviation (MAD)0.5
Skewness1.687597632
Sum28278.6
Variance1.020687352
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.609302893 × 10-10
2022-10-20T18:52:37.610654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q10.5
median1
Q31.7
95-th percentile3.3
Maximum8.1
Range8.1
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.010290727
Coefficient of variation (CV)0.80627192
Kurtosis3.604120291
Mean1.253039702
Median Absolute Deviation (MAD)0.5
Skewness1.687597632
Sum28278.6
Variance1.020687352
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.609302893 × 10-10
2022-10-20T19:30:51.117311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.41896
 
8.4%
0.51704
 
7.6%
0.31636
 
7.2%
0.61476
 
6.5%
0.71286
 
5.7%
0.81184
 
5.2%
0.91014
 
4.5%
11000
 
4.4%
1.1992
 
4.4%
1.2864
 
3.8%
Other values (62)9516
42.2%
ValueCountFrequency (%)
028
 
0.1%
0.1196
 
0.9%
0.2794
3.5%
0.31636
7.2%
0.41896
8.4%
0.51704
7.6%
0.61476
6.5%
0.71286
5.7%
0.81184
5.2%
0.91014
4.5%
ValueCountFrequency (%)
8.12
 
< 0.1%
82
 
< 0.1%
7.62
 
< 0.1%
7.32
 
< 0.1%
7.24
< 0.1%
72
 
< 0.1%
6.96
< 0.1%
6.62
 
< 0.1%
6.52
 
< 0.1%
6.46
< 0.1%
2022-10-20T18:52:38.164070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.41896
 
8.4%
0.51704
 
7.6%
0.31636
 
7.2%
0.61476
 
6.5%
0.71286
 
5.7%
0.81184
 
5.2%
0.91014
 
4.5%
11000
 
4.4%
1.1992
 
4.4%
1.2864
 
3.8%
Other values (62)9516
42.2%
ValueCountFrequency (%)
028
 
0.1%
0.1196
 
0.9%
0.2794
3.5%
0.31636
7.2%
0.41896
8.4%
0.51704
7.6%
0.61476
6.5%
0.71286
5.7%
0.81184
5.2%
0.91014
4.5%
ValueCountFrequency (%)
8.12
 
< 0.1%
82
 
< 0.1%
7.62
 
< 0.1%
7.32
 
< 0.1%
7.24
< 0.1%
72
 
< 0.1%
6.96
< 0.1%
6.62
 
< 0.1%
6.52
 
< 0.1%
6.46
< 0.1%
2022-10-20T19:30:51.300229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

CO 1st Max Hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.66691776
Minimum0
Maximum23
Zeros4213
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size176.4 KiB
2022-10-20T18:52:38.421279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

CO 1st Max Hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.66691776
Minimum0
Maximum23
Zeros4213
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size176.4 KiB
2022-10-20T19:30:51.430394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q321
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation9.049558096
Coefficient of variation (CV)0.9361368661
Kurtosis-1.476167096
Mean9.66691776
Median Absolute Deviation (MAD)6
Skewness0.4722208115
Sum218163
Variance81.89450174
MonotonicityNot monotonic
2022-10-20T18:52:38.577003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q321
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation9.049558096
Coefficient of variation (CV)0.9361368661
Kurtosis-1.476167096
Mean9.66691776
Median Absolute Deviation (MAD)6
Skewness0.4722208115
Sum218163
Variance81.89450174
MonotonicityNot monotonic
2022-10-20T19:30:51.515494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
04213
18.7%
233364
14.9%
11808
8.0%
61753
7.8%
221609
 
7.1%
51412
 
6.3%
71343
 
6.0%
21294
 
5.7%
211280
 
5.7%
8967
 
4.3%
Other values (14)3525
15.6%
ValueCountFrequency (%)
04213
18.7%
11808
8.0%
21294
 
5.7%
3712
 
3.2%
4500
 
2.2%
51412
 
6.3%
61753
7.8%
71343
 
6.0%
8967
 
4.3%
9400
 
1.8%
ValueCountFrequency (%)
233364
14.9%
221609
7.1%
211280
 
5.7%
20733
 
3.2%
19324
 
1.4%
1886
 
0.4%
1760
 
0.3%
1642
 
0.2%
1542
 
0.2%
1446
 
0.2%

CO AQI
Numeric time series

HIGH CORRELATION
MISSING
NON STATIONARY
SEASONAL

Distinct52
Distinct (%)0.004609520432585764
Missing11287
Missing (%)0.5001329315845445
Infinite0
Infinite (%)0.0
Mean11.509617941671838
Minimum0.0
Maximum59.0
Zeros16
Zeros (%)0.0007089684509039348
Memory size180672
2022-10-20T18:52:38.762891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
04213
18.7%
233364
14.9%
11808
8.0%
61753
7.8%
221609
 
7.1%
51412
 
6.3%
71343
 
6.0%
21294
 
5.7%
211280
 
5.7%
8967
 
4.3%
Other values (14)3525
15.6%
ValueCountFrequency (%)
04213
18.7%
11808
8.0%
21294
 
5.7%
3712
 
3.2%
4500
 
2.2%
51412
 
6.3%
61753
7.8%
71343
 
6.0%
8967
 
4.3%
9400
 
1.8%
ValueCountFrequency (%)
233364
14.9%
221609
7.1%
211280
 
5.7%
20733
 
3.2%
19324
 
1.4%
1886
 
0.4%
1760
 
0.3%
1642
 
0.2%
1542
 
0.2%
1446
 
0.2%

CO AQI
Numeric time series

HIGH CORRELATION
MISSING
NON STATIONARY
SEASONAL

Distinct52
Distinct (%)0.004609520432585764
Missing11287
Missing (%)0.5001329315845445
Infinite0
Infinite (%)0.0
Mean11.509617941671838
Minimum0.0
Maximum59.0
Zeros16
Zeros (%)0.0007089684509039348
Memory size180672
2022-10-20T19:30:51.590954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median9
Q315
95-th percentile30
Maximum59
Range59
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.842641321
Coefficient of variation (CV)0.7682827845
Kurtosis2.845435217
Mean11.50961794
Median Absolute Deviation (MAD)4
Skewness1.558665256
Sum129840
Variance78.19230554
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.281995571 × 10-8
2022-10-20T18:52:38.979744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median9
Q315
95-th percentile30
Maximum59
Range59
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.842641321
Coefficient of variation (CV)0.7682827845
Kurtosis2.845435217
Mean11.50961794
Median Absolute Deviation (MAD)4
Skewness1.558665256
Sum129840
Variance78.19230554
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.281995571 × 10-8
2022-10-20T19:30:51.685333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51146
 
5.1%
31102
 
4.9%
6972
 
4.3%
7790
 
3.5%
8708
 
3.1%
9684
 
3.0%
2559
 
2.5%
10534
 
2.4%
13528
 
2.3%
11492
 
2.2%
Other values (42)3766
 
16.7%
(Missing)11287
50.0%
ValueCountFrequency (%)
016
 
0.1%
1156
 
0.7%
2559
2.5%
31102
4.9%
51146
5.1%
6972
4.3%
7790
3.5%
8708
3.1%
9684
3.0%
10534
2.4%
ValueCountFrequency (%)
592
 
< 0.1%
572
 
< 0.1%
566
< 0.1%
554
 
< 0.1%
544
 
< 0.1%
526
< 0.1%
516
< 0.1%
502
 
< 0.1%
494
 
< 0.1%
4810
< 0.1%
2022-10-20T18:52:39.335988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51146
 
5.1%
31102
 
4.9%
6972
 
4.3%
7790
 
3.5%
8708
 
3.1%
9684
 
3.0%
2559
 
2.5%
10534
 
2.4%
13528
 
2.3%
11492
 
2.2%
Other values (42)3766
 
16.7%
(Missing)11287
50.0%
ValueCountFrequency (%)
016
 
0.1%
1156
 
0.7%
2559
2.5%
31102
4.9%
51146
5.1%
6972
4.3%
7790
3.5%
8708
3.1%
9684
3.0%
10534
2.4%
ValueCountFrequency (%)
592
 
< 0.1%
572
 
< 0.1%
566
< 0.1%
554
 
< 0.1%
544
 
< 0.1%
526
< 0.1%
516
< 0.1%
502
 
< 0.1%
494
 
< 0.1%
4810
< 0.1%
2022-10-20T19:30:51.891140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

Interactions

2022-10-20T18:52:18.709268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

Interactions

2022-10-20T19:30:41.325448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-20T18:52:39.579697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/2022-10-20T19:30:52.024818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-10-20T18:52:39.817653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-10-20T19:30:52.171335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-20T18:52:40.099369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-20T19:30:52.328903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-20T18:52:40.439500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-20T19:30:52.475191image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-20T18:52:40.743645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-20T19:30:52.627017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-20T18:52:41.418869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-20T19:30:52.750007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-20T18:52:19.071202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-20T19:30:41.522837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-20T18:52:19.777682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-20T19:30:41.892341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-20T18:52:20.187924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-20T19:30:42.121649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-20T18:52:20.381385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-20T19:30:42.225552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCO MeanCO 1st Max ValueCO 1st Max HourCO AQI
041330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-01Parts per billion19.04166749.01946Parts per million0.0225000.0401034Parts per billion3.0000009.02113.0Parts per million1.1458334.221NaN
141330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-01Parts per billion19.04166749.01946Parts per million0.0225000.0401034Parts per billion3.0000009.02113.0Parts per million0.8789472.22325.0
241330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-01Parts per billion19.04166749.01946Parts per million0.0225000.0401034Parts per billion2.9750006.623NaNParts per million1.1458334.221NaN
341330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-01Parts per billion19.04166749.01946Parts per million0.0225000.0401034Parts per billion2.9750006.623NaNParts per million0.8789472.22325.0
441330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-02Parts per billion22.95833336.01934Parts per million0.0133750.0321027Parts per billion1.9583333.0224.0Parts per million0.8500001.623NaN
541330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-02Parts per billion22.95833336.01934Parts per million0.0133750.0321027Parts per billion1.9583333.0224.0Parts per million1.0666672.3026.0
641330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-02Parts per billion22.95833336.01934Parts per million0.0133750.0321027Parts per billion1.9375002.623NaNParts per million0.8500001.623NaN
741330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-02Parts per billion22.95833336.01934Parts per million0.0133750.0321027Parts per billion1.9375002.623NaNParts per million1.0666672.3026.0
841330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-03Parts per billion38.12500051.0848Parts per million0.0079580.016914Parts per billion5.2000008.320NaNParts per million1.7625002.5828.0
941330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-03Parts per billion38.12500051.0848Parts per million0.0079580.016914Parts per billion5.2000008.320NaNParts per million1.9291674.48NaN

Last rows

State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCO MeanCO 1st Max ValueCO 1st Max HourCO AQI
2255841330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2015-12-29Parts per billion25.00000046.02043Parts per million0.0160420.0371034Parts per billion1.4347834.006.0Parts per million0.6666671.3215.0
2255941330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2015-12-29Parts per billion25.00000046.02043Parts per million0.0160420.0371034Parts per billion1.5000003.02NaNParts per million0.6260871.40NaN
2256041330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2015-12-30Parts per billion28.90476250.01947Parts per million0.0138750.0361033Parts per billion1.1571432.323NaNParts per million0.6260871.621NaN
2256141330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2015-12-30Parts per billion28.90476250.01947Parts per million0.0138750.0361033Parts per billion1.1571432.323NaNParts per million0.6125000.9110.0
2256241330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2015-12-30Parts per billion28.90476250.01947Parts per million0.0138750.0361033Parts per billion1.1304353.0214.0Parts per million0.6260871.621NaN
2256341330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2015-12-30Parts per billion28.90476250.01947Parts per million0.0138750.0361033Parts per billion1.1304353.0214.0Parts per million0.6125000.9110.0
2256441330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2015-12-31Parts per billion34.12500045.0942Parts per million0.0100000.0191018Parts per billion2.0416675.087.0Parts per million0.9583331.3215.0
2256541330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2015-12-31Parts per billion34.12500045.0942Parts per million0.0100000.0191018Parts per billion2.0125004.08NaNParts per million0.9250002.08NaN
2256641330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2015-12-31Parts per billion34.12500045.0942Parts per million0.0100000.0191018Parts per billion2.0416675.087.0Parts per million0.9250002.08NaN
2256741330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2015-12-31Parts per billion34.12500045.0942Parts per million0.0100000.0191018Parts per billion2.0125004.08NaNParts per million0.9583331.3215.0